97 lines
3.6 KiB
Python
97 lines
3.6 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class VideoClassifierV3_15(nn.Module):
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def __init__(self, embedding_dim=1024, hidden_dim=648, output_dim=3, temperature=1.7):
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super().__init__()
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self.num_channels = 3
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self.channel_names = ['title', 'description', 'tags']
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# 可学习温度系数
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self.temperature = nn.Parameter(torch.tensor(temperature))
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# 带约束的通道权重（使用Sigmoid替代Softmax）
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self.channel_weights = nn.Parameter(torch.ones(self.num_channels))
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# 增强的非线性层
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self.fc = nn.Sequential(
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nn.Linear(embedding_dim * self.num_channels, hidden_dim*2),
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nn.BatchNorm1d(hidden_dim*2),
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nn.Dropout(0.2),
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nn.GELU(),
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nn.Linear(hidden_dim*2, output_dim)
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)
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# 权重初始化
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self._init_weights()
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def _init_weights(self):
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for layer in self.fc:
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if isinstance(layer, nn.Linear):
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# 使用ReLU的初始化参数（GELU的近似）
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nn.init.kaiming_normal_(layer.weight, nonlinearity='relu') # 修改这里
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# 或者使用Xavier初始化（更适合通用场景）
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# nn.init.xavier_normal_(layer.weight, gain=nn.init.calculate_gain('relu'))
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nn.init.zeros_(layer.bias)
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def forward(self, channel_features: torch.Tensor):
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"""
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输入格式: [batch_size, num_channels, embedding_dim]
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输出格式: [batch_size, output_dim]
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"""
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# 自适应通道权重（Sigmoid约束）
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weights = torch.sigmoid(self.channel_weights) # [0,1]范围
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weighted_features = channel_features * weights.unsqueeze(0).unsqueeze(-1)
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# 特征拼接
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combined = weighted_features.view(weighted_features.size(0), -1)
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return self.fc(combined)
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def get_channel_weights(self):
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"""获取各通道权重（带温度调节）"""
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return torch.softmax(self.channel_weights / self.temperature, dim=0).detach().cpu().numpy()
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class AdaptiveRecallLoss(nn.Module):
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def __init__(self, class_weights, alpha=0.8, gamma=2.0, fp_penalty=0.5):
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"""
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Args:
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class_weights (torch.Tensor): 类别权重
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alpha (float): 召回率调节因子（0-1）
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gamma (float): Focal Loss参数
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fp_penalty (float): 类别0假阳性惩罚强度
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"""
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super().__init__()
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self.class_weights = class_weights
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self.alpha = alpha
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self.gamma = gamma
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self.fp_penalty = fp_penalty
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def forward(self, logits, targets):
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# 基础交叉熵损失
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ce_loss = F.cross_entropy(logits, targets, weight=self.class_weights, reduction='none')
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# Focal Loss组件
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pt = torch.exp(-ce_loss)
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focal_loss = ((1 - pt) ** self.gamma) * ce_loss
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# 召回率增强（对困难样本加权）
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class_mask = F.one_hot(targets, num_classes=len(self.class_weights))
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class_weights = (self.alpha + (1 - self.alpha) * pt.unsqueeze(-1)) * class_mask
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recall_loss = (class_weights * focal_loss.unsqueeze(-1)).sum(dim=1)
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# 类别0假阳性惩罚
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probs = F.softmax(logits, dim=1)
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fp_mask = (targets != 0) & (torch.argmax(logits, dim=1) == 0)
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fp_loss = self.fp_penalty * probs[:, 0][fp_mask].pow(2).sum()
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# 总损失
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total_loss = recall_loss.mean() + fp_loss / len(targets)
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return total_loss |